Modern AI agents operate with authority — executing tools, accessing credentials, and interacting with external systems. Many defenses focus on detecting malicious inputs. MVAR instead enforces deterministic security boundaries at execution sinks, where privileged actions occur.

Core design principle: separate influence from authority.

Untrusted data may influence reasoning; privileged execution is governed by policy invariants.

MVAR implements three enforcement layers:

1. Provenance-based information flow control All data carries integrity and confidentiality labels with conservative propagation. Policy decisions derive from data lineage rather than payload inspection.

2. Capability-based runtime constraints No ambient authority. Tools execute within explicitly declared permissions. Targets are enforced individually (e.g., api.gmail.com ≠ arbitrary domains).

3. Deterministic sink policy evaluation Privileged actions are evaluated against strict invariants:

UNTRUSTED + CRITICAL → BLOCK

Decisions are deterministic and produce evaluation traces.

When enabled, decisions may be cryptographically signed (QSEAL Ed25519) for tamper-evident auditability.

Validation

Evaluated against a reproducible 50 vector adversarial corpus spanning nine attack categories (command injection, encoding/obfuscation, multi-stage execution, credential theft, etc.).

Validation suite runs locally in ~2 minutes.

Scope, assumptions, and limitations are explicitly documented in THREAT_MODEL.md.

This release represents Phase 1, focused on deterministic enforcement rather than detection or behavioral scoring. Composition attacks and automatic sink discovery are future work.

Open source (Apache 2.0).

Repository: https://github.com/mvar-security/mvar Site: https://mvar.io